翻译：
This paper explains the basic concepts of data governance and data assets related theoretical knowledge
Basic theoretical knowledge
- Data
Data, or data resources, refers to the general term for all the symbols that can be input into the computer and processed by the computer program. It is a general term for numbers, letters, symbols and analog quantities that have a certain meaning and are used to input electronic computers for processing. It is the most basic element of an information system.
- Big data
Big Data refers to a data set with a large scale that greatly exceeds the capability range of traditional database software tools in terms of acquisition, storage, management and analysis. It has four characteristics: massive data scale, rapid data flow, diverse data types and low value density.
Big data includes structured, semi-structured, and unstructured data, with unstructured data increasingly becoming a major part of the data. Big data technology is not about mastering huge data information, but about specialized processing of these meaningful data. In other words, if the big data is compared to an industry, then the key to achieving profitability in this industry is to improve the “processing capacity” of the data, and realize the “value-added” of the data through “processing”.
- Data Sources
A Data Source is a device or raw medium that provides some kind of desired data. All information to establish a database connection is stored in the data source. Just as you can find a file in the file system by specifying a file name, you can find the corresponding database connection by providing the correct data source name.
Common data source types are relational database, temporal database, key-value storage database, column storage database, document database, graph database, search engine storage, object database, MPP database, large database, tool or file, etc.
- Data warehouse
A Data Warehouse is a collection of data that supports all types of data at all levels of the enterprise’s decision making process. In general, it is the primary function of data storage, in order to output analytical reports to the organization, for the purpose of supporting decision making. At the same time, it can also provide guidance for business process improvement, monitoring and managing data access time, data cost, and data quality.
Since the data warehouse is a data storage space for data summary, under normal conditions, the data warehouse will be layered, common layers are paste source layer (ODS), data integration layer (EDW), topic model layer (FDM), common computing layer/common processing layer (ADM), application market layer/data market layer (ADS). Each layered combination will complete the hierarchical design of the data warehouse according to the specific implementation.
- Data center
Data center is a set of sustainable “let the enterprise data use” mechanism, a strategic choice and organizational form, is based on the enterprise’s unique business model and organizational structure, through tangible products and implementation methodology support, to build a set of continuous data into assets and serve the business mechanism. The data center needs to have four core capabilities of data aggregation and integration, data purification and processing, data service visualization, and data value realization, so that employees, customers, and partners of the organization can easily apply data.
Data center is a concept, theory, and not the name of an independent system, it is a new concept derived from the data warehouse (data center). Functional positioning is the place where all data is gathered, and the platform foundation that provides support for upper-level data applications, that is, data empowerment.
If you want to fully understand the data center and distinguish the similarities and differences between the data warehouse and the data center, it is necessary to explain from three levels: data source, construction goal and data application.
At the data source level:
The data source of data warehouse is mainly business database, and the data format is mainly structured data.
The data source of the data center is expected to be global data including business data, log data, buried data, crawler data, external data, etc. The data format can be structured or unstructured.
At the level of construction objectives:
The construction of data warehouse is mainly used to make BI reports, with a single purpose, and only extract and clean the basic data of the relevant analysis reports. If a new report is added, data processing needs to be done from ODS to ADS.
The goal of establishing a data center is to integrate all the data of the organization, open up the gap between the data, and eliminate the problem of inconsistent data standards and caliber. The data center usually cleans the basic data from various aspects, and establishes a number of theme-based theme domains according to the concept of theme domains, such as user theme domains, commodity theme domains, channel theme domains, store theme domains, and so on. The data center follows three one concepts: One Data, One ID, One Service. Based on this concept, the data center not only gathers various data of the enterprise, but also makes these data follow the same standard and caliber, and can unify or interrelate the identification of things, and provide a unified data service interface to complete data empowerment.
At the data application level:
The data warehouse is mainly oriented to BI reports, and the construction of data application is the traditional chimney construction and the development way of starting from scratch every time.
The data application on the data platform is not only for BI reports, but also for marketing recommendation, user portrait, AI decision analysis, risk assessment, business analysis, etc. And these data applications, based on the data center has been summarized and precipitated, can quickly provide data for related systems, complete rapid data development work, and previous work results can be shared by multiple applications.
- Data management
Data Management is the planning, implementation, and oversight of policies, practices, and programs to capture, control, protect, deliver, and enhance the value of data and information assets.
It generally contains the following three meanings:
Data management encompasses a range of business functions, including the planning and execution of policies, programs, practices and projects;
(2) Data management includes a strict set of management practices and processes to ensure that business functions are effectively performed;
(3) Data management consists of multiple management teams composed of business leaders and technical experts who are responsible for implementing management norms and processes.
- Data governance
According to the International Data Management Association (DAMA), data governance is the collection of activities that exercise authority and control over the management of data assets.
The definition given in GBT34960.5-2018 Information Technology Service Governance Part 5 is: the collection of relevant control activities, performance and risk management in the process of data resources and their application. The data governance domain includes data management system and data value system.
As defined by the International Data Governance Institute (DGI) : Data governance is a system that implements the division of decision rights and responsibilities through a set of information-related processes that are executed according to a consensus model that describes Who can act on What information, When and Where, with what methods and what actions.
Another explanation:
Chivalrous data governance to meet the needs of internal risk management and external regulatory compliance. A system in which decision rights and responsibilities are assigned through a series of information-related processes.
Data governance in a broad sense is the set of activities that exercise authority and control over the management of data assets (planning, monitoring, and enforcement), direct how other data management functions are performed, and enforce the data management system at a high level. A series of ongoing processes undertaken by the organization to maximize the value of data assets, clarify the responsibilities of data stakeholders, align data stakeholders’ interests, and facilitate joint data actions by data stakeholders.
The ultimate goal is to enhance the value of data, data governance is very necessary, is the foundation of enterprises to achieve digital strategy, it is a management system, including organization, system, process, tools.
Here’s another explanation:
Data Governance refers to a series of concrete work that takes data as an enterprise asset, and it is the whole life cycle management of data.
Data governance is divided into data and governance from the phrase composition, and governance has the meaning of reform. Since there is a reform, it is necessary to have relevant systems, processes and tools to complete the re-sorting and classification of data to meet the requirements of data use.
The goal of data governance is to improve data quality (accuracy and integrity), ensure data security (confidentiality, integrity and availability), and realize the sharing of data resources among organizations and departments. Promote the integration, docking, sharing and comprehensive application of information resources, so as to improve the level of enterprise management and give full play to the role of information technology in operation and management.
Data governance related systems and processes will lead to data governance consulting, Such as “Data governance organizational Structure and Talent Management Plan”, “Data Governance Implementation Path”, “Data Application Scenario Implementation Path”, “Metadata management Methods and processes”, “Data standard Management Methods and processes”, “Data quality problem analysis and rectification Plan”, “Data governance Development Plan for the next N years”, etc.; Data governance tools will lead to related management systems, such as metadata management systems, data security systems, data standards systems, data quality systems, etc., and generally tend to complete the implementation and landing of current data governance based on data governance consulting results.
- Data assets
Data Asset refers to the data resources, such as documents and electronic data, that are owned or controlled by an enterprise and can bring future economic benefits to the enterprise. In the enterprise, not all data constitute data assets, data assets are data resources that can generate value for the enterprise.
GBT34960.5-2018 Information Technology Services Governance Part 5: Data resources owned and controlled by an organization that can produce benefits.
Theoretical knowledge of data governance
- Data model
A Data Model, often referred to simply as a model, is an abstraction of real-world data features used to describe the concepts and definitions of a set of data. The data model describes the static characteristics, dynamic behavior and constraints of data from the level of abstraction. The content described by the data model consists of three parts: data structure, data operation (among which there is no data operation in the ER graph data model) and data constraints, which form the basic blueprint of the data structure and also the strategic map of the enterprise data assets. According to different application levels, the data model is divided into four types: subject domain data model, conceptual data model, logical data model and physical data model.
Subject domain data model: referred to as the subject domain model, is the planning blueprint of the highest perspective, and is an abstraction that synthesizes, classifies, analyzes and utilizes the data in the enterprise information system at a higher level. In general, the topic domain model is divided into business, system and department.
Conceptual data model: referred to as conceptual model, is a user-oriented and objective world oriented model, mainly used to describe the conceptual structure of the real world, and has nothing to do with the specific Database Management System (DBMS, Database Management System), generally only entity set, contact set analysis structure.
Logical data model: referred to as logical model, is a conceptual model based on business lines, business matters, business processes, business scenarios based on the needs of the design of business-oriented data model, generally including specific functions and processing information. Logical models are DBMS-oriented models that guide implementation in different DBMS systems. The common forms of logical data model include network data model, hierarchical data model and so on.
Physical data model: A physical model for short, is a model for computer physical representation that describes the organization of data on a storage medium. The design of the physical model should be based on the results of the logical model to ensure that the business requirements are met. It is not only related to the specific DBMS, but also related to the operating system and hardware, so it is necessary to consider the related requirements of system performance when designing the model.
- Metamodel & Metadata
A Meta Model is a model about a model, a specification that describes a model, specifically the elements that make up the model and the relationships between them. Metamodel is a concept relative to model, without which metamodel has no meaning.
Metadata, also known as intermediary data and relay data, describes data about data, mainly describes data properties, and supports functions such as indicating storage locations, historical data, resource search, and file records. Metadata is information about the organization of data, its domains, and its relationships; in short, metadata is data about data. Metadata is divided into technical metadata, service metadata, operational metadata, and management metadata.
The relationship between data model, metamodel and metadata: The model is the abstraction of data characteristics and the theoretical basis for the formation of metamodel. Metamodel is the metadata model and the data model that stores metadata. Due to the diversity of metadata, the metamodel corresponding to different types and subclasses is not the same, and needs to be designed according to specific metadata.
Technical metadata
Technical Metadata: Data that describes concepts, relationships, and rules related to a technical domain in adata system; It includes the definition of objects and data structures in the data platform, the mapping from source data to destination data, and the description of data transformation.
Technical metadata, if subdivided, can also be divided into structural technology metadata and associative technology metadata.
Structural technology metadata: Structural technology metadata provides a description of the data in the information technology infrastructure, such as where the data is stored, the type of storage of the data, and the blood relationship of the data.
Relational technology metadata: describes the relationship between data and the flow of data in the information technology environment. The scope of technical metadata mainly includes: technical rules (calculation/statistics/transformation/summary), technical description of data quality rules, fields, derived fields, facts/dimensions, statistical indicators, tables/views/files/interfaces, reports/multidimensional analysis, databases/view groups/file groups/interface groups, source code/programs, systems, software, hardware, etc.
In practice, the content of technical metadata collection will be adjusted according to the specific content of different databases, such as the common tables, fields, stored procedures, functions, views of relational databases, and key-value storage databases have the concept of views and stored procedures.
Service metadata
Data that describes concepts, relationships, and rules related to the business domain in the data system; Including business terms, information classification, indicators, statistical caliber and so on. On the other hand, business metadata is the key metadata of the data warehouse environment, the way users access business data, from a variety of sources: Case Modeling tools, control databases, database catalogs, and data extraction/transformation/loading tools.
In practice, common data indicators, data elements (data elements), data labels, and report headers belong to business metadata.
Operational metadata
Organization, jobs, responsibilities, processes related to metadata management, and operational data generated by the daily operation of the system. The content of operational metadata management mainly includes: the organization, post, responsibility, process, project, version related to metadata management, and the operation records in the production operation of the system, such as operation records, applications, and operation jobs.
Simply understood, operational metadata is data that describes the process of data processing.
In practice, the main data stored by the general operation metadata are: data ETL information, data processing strategy information, data processing scheduling information, data processing exception information, etc.
Management metadata
It describes the management attributes of data, including the management department, the management responsible person, etc. By clarifying the management attributes, it is conducive to the responsibility of data management to departments and individuals, and is the basis of data security management. Common management metadata include: data owner, data quality determination, data security level, and so on.
In simple terms, administrative metadata is data that describes where data management belongs.
In practice, the main data stored by the general management metadata is: data ownership information (business ownership, system ownership, operation and maintenance ownership, data rights ownership), and the permission information (including data security information) of users’ access to libraries, tables, views, stored procedures, etc. created in each database.
How to define the metamodel based on different business scenarios and database types, how to collect technical metadata, business metadata, and operational metadata, etc., will be detailed in the subsequent compilation of the article “Metadata Management System Construction”.
- Data standards
Data Standards are normative constraints that ensure consistency and accuracy in the internal and external use and exchange of data. In the process of digitization, data is the real reflection of business activities in the information system. Since business objects exist in the form of data in information systems, management activities related to data standards need to be based on business, and standardize the unified definition and application of business objects in various information systems in the form of standards, so as to improve the capabilities of enterprises in business collaboration, regulatory compliance, data sharing and openness, data analysis and application.
Data standard is a standardized system reached from the three aspects of business, technology and management, and it is also the establishment of a set of standardization system that conforms to its own reality, covering the definition, operation and application of multi-level data. It includes basic class standard and index class standard.
Basic class data standard
Basic data standards are designed to unify the consistency and accuracy of data related to all business activities of the organization, to solve inter-business data consistency and data integration, to solve inter-business data consistency and data integration according to the data standard management process, and to develop data standards according to the data standard management process.
The main content of the basic class data standard, including data elements, code sets, data sets, coding rules.
A Data Element, also known as a data element, is a data unit that describes its definition, identification, representation, and allowable values with a set of attributes. In a given context, it is usually used to construct a semantically correct, independent, and unambiguous information unit for a specific conceptual semantics. Data elements can be understood as the basic unit of data. The data model is composed of several relevant data elements in a certain order. The corresponding is the data element standard.
A code set is a categorical encoding of data elements used to describe a basic data set of information. The CODE is a collable data set based on the classification coding of a certain code set. In general, the code is an unordered object set, containing the unique VALUE code and the corresponding value value. For extensibility, reflecting the tree CODE pattern, there will also be a parent class code. Because the term code in the understanding of business people creates the concept of developing code, sometimes the code set is changed to the code set, corresponding to the coding standard.
Index class data standards
Index data standards are generally divided into basic index standards and calculated indicators (also known as composite indicators). Basic indicators have specific business and economic meanings and can be obtained only through the processing of basic data. The calculated indicators are usually calculated by more than two basic indicators.
- Data quality
Data Quality is the basis to ensure the application effect of data, and it is an indicator to describe the value content of data.
There are many index systems to measure data quality, typical indicators are: Integrity (whether the data is missing), normalization (whether the data is stored according to the required rules), consistency (whether the data values have conflicting information meanings), accuracy (whether the data is wrong), uniqueness (whether the data is duplicated), timeliness (whether the data is uploaded according to the time requirements).
Data quality problems are usually looked for from the technical side, the business side, and the management side.
Technical aspect
In terms of technology, data quality problems are generally found at various points in the whole data life cycle of database table design, data production, data collection, data transmission, data loading and data storage.
Database table design: Unreasonable design of table structure, field constraints, and data verification rules during the construction of the business system, resulting in no or improper verification of data entry, causing data duplication, inaccuracy, and incompleteness.
Data production process: refers to the production data generated by the business system, the data write permission is not controlled in the business system, the data collection page is not verified, the data submission is not restricted, the logic between the data is not controlled, and the data is repeated, inaccurate, and inconsistent. Each business system is common or dependent on data without unified management, each business system is independent, chimney construction system, resulting in data inconsistency between systems.
Data collection process: Data is obtained through API, DB Link and other methods, and unreasonable Settings of collection point, collection frequency, collection content, mapping relationship, collection parameters and process result in low data collection efficiency, collection failure, data loss, data mapping and conversion failure, etc.
Data transmission link: The network is uncontrollable and the data transmission process is not encrypted, resulting in data quality problems caused by data tampering and loss in the data transmission link.
Data processing links: refers to ETL, data development and other ways, in the preparation of data cleaning rules, data conversion rules, data loading rules, do not do reasonable restrictions, verification and other ways, resulting in data duplication, mapping errors and other problems.
Data storage link: unreasonable data storage area Settings, artificial data adjustment on the data storage, causing data loss, invalid, distortion, duplication and other problems.
Business aspect
On the business side, data quality problems are caused by unclear requirements, frequent changes in requirements, non-standard data input formats, and data fraud.
Unclear requirements: business rules, business processes, and business collection information items are not clear, which affects the unreasonable data model built in the design process, and then leads to data quality problems in the data production process.
Frequent changes in demand: It is generally due to unclear requirements that lead to frequent changes in demand, affecting all aspects of the data at the technical level. In the case of frequent changes, slight negligence or unreasonable design or incorrect data migration logic lead to frequent data quality problems and difficult governance.
Non-standard data input format: Generally, it mainly applies to the input scenario of a large range of content data. Due to the lack of attention to the upper and lower case, full half Angle, and special characters of the input content, data distortion and data loss are caused.
Data fraud: In order to improve or reduce the assessment indicators, or quickly complete the relevant data collection work, the operator processed some data when entering, so that the authenticity of the data could not meet the quality requirements.
Management aspect
In terms of management, it is mainly due to weak cognition of data quality, lack of or failure to implement data quality systems, lack of data accountability and data assessment mechanisms, resulting in data quality problems caused by lack of data management.
Data quality cognition: do not recognize the importance of data quality, pay attention to system construction lack of attention to data production, think that the system is omnipotent, poor data quality does not matter.
Data quality system: There is no unified process and system support for data quality problems from input, discovery, assignment, processing and optimization, resulting in non-standard data, data loss, data conflict and other problems in data production. There is no control and management for subsequent data discovery, indicators, processing and optimization, and there is no corresponding data recognition and assessment mechanism to restrict behavior when data problems occur. As a result, the whole data quality problem does not form a closed loop.
The influence of data quality can also be analyzed from objective factors and subjective factors. In the process of data flow, data quality problems are caused by objective factors such as system anomalies and improper process Settings. In the process of data processing, subjective factors such as low data awareness and management defects cause data quality problems caused by improper operation.
- Data exchange
In the scenario based on Data center, data warehouse, and data governance, Data Switching does not refer to the process of establishing a temporary interconnect path for data communication between any two terminal devices (Dtes). It refers to the integration of data in a number of decentralized application information systems, so that several application subsystems can transmit and share information/data, improve the utilization rate of information resources, and become the basic goal of information construction to ensure the interconnection between distributed and heterogeneous systems.
In simple understanding, the current data exchange mainly applies the data generated by the system to complete the interconnection between heterogeneous databases (sources) through data unloading and data loading. Common data exchange modes are library to library, library to file, file to library, file to file.
- Data services
Data Service (Data Service) is the ability to provide all-enterprise-level data services, through the service packaging, to provide data to business systems in the form of service interfaces.
In addition to integrating data services in various places to achieve unified docking and export of data services, data apis can also be configured based on data services. Through unified access and unified management, enterprise-level data services can be issued, applied for, docking and invocation, authentication, monitoring, and traffic limiting management and control, so as to achieve unified management and control of data services.
Data services provide secure and unified data for data users from the system application level.
- Data life cycle
Everything has a certain life cycle, and data is no exception. Data Life Cycle refers to the generation, processing, use and even extinction of data. Based on a scientific management method, the data that is rarely or no longer used is separated from the system and retained through verified storage devices, which can not only improve the operation efficiency of the system, but also better serve customers. It can also significantly reduce storage costs caused by long-term data storage.
The data life cycle generally includes the online stage, the archiving stage (sometimes further divided into online archiving stage and offline archiving stage), and the destruction stage. The management content includes the establishment of reasonable data categories, and the formulation of retention time, storage media, cleaning rules and methods, and precautions for different types of data at each stage.
- Data development
Data Development refers to the activities of building unified and standardized tool capabilities for the whole process around the whole life cycle of data, and conducting unified management of data model design, data processing program development, testing, and launching. In general, data development includes offline development and real-time development.
Offline development, also known as offline data development, refers to the processing of data yesterday or more ago by compiling data processing expressions, the time unit is usually days, hours.
Real-time development, also known as real-time data development, processes data received immediately, the timeliness mainly depends on the transmission and storage speed, the time unit is usually seconds, milliseconds.
- Data security
Data Security The security of technology and management established and adopted for data processing systems to protect computer hardware, software, and data from damage, alteration, and disclosure for accidental or malicious reasons. Therefore, the security of computer network can be understood as: through the use of various technical and management measures, the normal operation of the network system, so as to ensure the availability, integrity and confidentiality of network data.
Data classification catalog, also known as data catalog, refers to the process of distinguishing and classifying organizational data according to certain principles and methods according to the attributes or characteristics of the organization, and establishing a certain classification system and arrangement order in order to better manage and use organizational data.
Data catalog is a key part of data protection work, is the basis for establishing a unified, accurate and perfect data architecture, is the basis for realizing centralized, specialized and standardized data management, and is also an important data dependence for data asset inventory.
Graph data classification (Example)
Data classification, also known as sensitive classification, refers to the process of distinguishing the importance and sensitivity differences of data using standardized and clear methods on the basis of data classification, and grading it according to certain classification principles, so as to provide support for the establishment of security policies for data opening and sharing.
Figure 3 Sorting out data levels based on the Data Security Grading Guide for Financial Data Security (example)
In static desensitization, data is extracted for desensitization and sent to the test library. The desensitized data is isolated from the production environment to meet service requirements and ensure the security of the production database. Static desensitization is an irreversible action, which can be summarized as “moving and simulating replacement” of data.
Dynamic desensitization, based on desensitization rules, desensitizes the query and call results of sensitive data in real time to ensure the availability and security of returned data. Dynamic desensitization can be summarized as “desensitization while using”.
Theoretical knowledge of data assets
- Service data
Business Data is the data generated in the process of business processing or transaction processing, also known as transaction data. There are mainly three types of business data generation: First, the data generated in the process of business transactions, such as: planning order, sales order, production order, purchase order, etc., most of these data are generated manually; 2. Data generated by the system, including hardware running status, software running status, resource consumption status, application usage status, interface call status, service health status, etc.; Third, the data generated by automation equipment, the operation data of various types of equipment in the IOT Internet, production collection data, and so on. No matter where the source, the data here has a common feature is strong timeliness, high response, and large amount of data.
- Master data
Master Data refers to the data used to describe the core business entity of the enterprise, and is the execution subject of the enterprise’s core business object and transaction business. It is the high-value basic data that is repeated and shared across the entire value chain and applied to multiple business processes, across various business departments and systems, and is the basis for data interaction between various business applications and systems. From a business perspective, master data is relatively “fixed” and changes slowly. Master data is the nerve center of enterprise information system and the basis of business operation and decision analysis. For example, customers, corporate organizations and employees, products, channels, subjects, etc.
- Data value
Data Value is the measurement of the intrinsic value of data, which can be carried out from the two aspects of data cost and data application value. Data costs generally include collection, storage and calculation costs (labor costs, IT equipment and other direct costs and indirect costs, etc.) and operation and maintenance costs (business operation costs, technical operation costs, etc.). The application value of data is mainly measured from the classification of data, frequency of use, object of use, effect of use and sharing circulation.
- Asset catalog
A Data Asset Catalog (hereinafter referred to as an asset catalog) is a catalog system that extracts valuable data that can be used for analysis and application. Compiling data asset catalog is mainly to establish the relationship between business scenarios and data resources, and reduce the threshold of understanding system data.
Correlation
- Relationship between data management, data governance and data assets
Data management includes data governance, and the concept that governance is part of overall data management has been widely recognized by the industry. Data management encompasses a number of different areas, one of the most prominent being data governance. Data assets are based on data governance, and the core is how to realize data value, reflect data value, and complete data empowerment. The relationship among data management, data governance and data asset management is shown in the figure.
Figure 4 Relationships among data management, data governance, and data asset management
- Data governance framework
GB/T34960 “Information Technology Service Governance” Part 5 mentioned that the data governance framework includes four parts: top-level design, data governance environment, data governance domain and data governance process.
Figure 5 Data governance framework
Top-level design includes data-related strategic planning, organization building, and architecture design, and is the foundation of data governance implementation. Data governance environment includes internal and external environment and contributing factors, which is the guarantee of data governance implementation. Data governance domain includes data management system and data value system, and is the object of data governance implementation. The data governance process, which includes planning and planning, building and running, monitoring and evaluation, and improvement and optimization, is the method of data governance implementation.
In the data governance domain, the main organization of the data management system should focus on data standards, data quality, data security, metadata management and data life cycle, etc., to carry out the governance of the data management system, including at least: a) assess the status quo and ability of data management, analyze and evaluate the maturity of data management; b) Guide the implementation of the data management system governance plan to meet data strategy and management requirements; c) Monitor the performance and compliance of data management and continuously improve and optimize it.
Data value system The main organization should focus on data circulation, data services and data insights, and conduct data asset operation and application governance, including at least: a) evaluate the operation and application capabilities of data assets, and support the transformation and realization of data value; b) Guide the implementation of the data value system governance scheme to meet the operation and application requirements of data assets; c) Monitor the performance and compliance of data value realization, and continuously improve and optimize.
- The relationship between data governance, data assets and data
From the data level, the data system includes three parts: governance, management and application. Governance is responsible for solving the problems between people, people and data, management is responsible for various functional areas, and application is the realization of data value. According to these three dimensions, data governance focuses on governance, which generally includes data governance consultation and data governance implementation, and is the combination of data in governance and management. Data assets focus on assets, generally focusing on the value of data and the application of data, based on data asset inventory and value analysis, show the value of data assets and provide data applications.
In other words, data governance is the set of activities (planning, monitoring and execution) that exercise authority and control over data at a high level, and data assets focus on discovering the value of data, helping enterprises develop and improve their operational capabilities by providing the ability to apply data.
Data is the raw material of enterprise informatization, and data governance is the cornerstone of enterprise informatization. Data assets are based on the data of data governance, mining the value of data, empowering enterprises through data operation and data analysis, and helping the take-off of enterprise informatization.
